Title BrowseVCF: a web-based application and workflow to quickly prioritise disease-causative ...

12 Pages·2016·0.46 MB·English

by  Silvia Salatino

bioRxiv preprint first posted online Dec. 17, 2015; doi: http://dx.doi.org/10.1101/034769. The copyright holder for this preprint
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY 4.0 International license.
Title
BrowseVCF: a web-based application and workflow to quickly prioritise disease-causative variants
in VCF files.

Authors
Silvia Salatino1,*, Varun Ramraj1.

Author information
1 The Wellcome Trust Centre for Human Genetics, Roosvelt Drive, Oxford OX3 7BN, UK
* Corresponding author

Corresponding author’s information
Name: Silvia Salatino
Email address: [email protected]

Running title
A web-based tool to filter variants from VCF files

Keywords
VCF; variant filtering; variant analysis; exome sequencing; whole-genome sequencing;
prioritisation.

1
bioRxiv preprint first posted online Dec. 17, 2015; doi: http://dx.doi.org/10.1101/034769. The copyright holder for this preprint
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY 4.0 International license.
Abstract
As sequencing costs associated with fast advancing Next Generation Sequencing (NGS)
technologies continue to decrease, variant discovery is becoming a more affordable and popular
analysis method among research laboratories. Following variant calling and annotation, accurate
variant filtering is a crucial step to extract meaningful biological information from sequencing data
and to investigate disease etiology. However, the standard variant call file format (VCF) used to
store this valuable information is not easy to handle without bioinformatics skills, thus preventing
many investigators from directly analysing their data. Here, we present BrowseVCF, an easy-to-
use stand-alone software that enables researchers to browse, query and filter millions of variants in
a few seconds. Key features include the possibility to store intermediate search results, to query
user-defined gene lists, to group samples for family or tumour/normal studies, to download a report
of the filters applied, and to export the filtered variants in spreadsheet format. Additionally,
BrowseVCF is suitable for any DNA variant analysis (exome, whole-genome and targeted
sequencing), can be used also for non-diploid genomes, and is able to discriminate between Single
Nucleotide Polymorphisms (SNPs), Insertions/Deletions (InDels), and Multiple Nucleotide
Polymorphisms (MNPs). Owing to its portable implementation, BrowseVCF can be used either on
personal computers or as part of automated analysis pipelines. The software can be initialised with
a few clicks on any operating system without any special administrative or installation permissions.
It is actively developed and maintained, and freely available for download from
https://github.com/BSGOxford/BrowseVCF/releases/latest.

Introduction
Recent developments of NGS technologies have led to a dramatic reduction in sequencing costs
that, in turn, made DNA sequencing analyses accessible to small and medium-sized laboratories.
Consequently, increasing amounts of sequencing data need to be analysed quickly and require
specific bioinformatics expertise. Whilst large institutes might be able to rely on support from
dedicated bioinformatics core units, wet-lab researchers from smaller centres may struggle to find
easy-to-use tools for variant analysis. The variant call format (VCF), originally developed for the
1000 Genomes Project, has become the standard for storing DNA variants together with rich
2
bioRxiv preprint first posted online Dec. 17, 2015; doi: http://dx.doi.org/10.1101/034769. The copyright holder for this preprint
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY 4.0 International license.
annotations (Danecek et al. 2011), and has led to the development of a variety of command-line
analysis tools (for instance, VCFtools (Danecek et al. 2011) or GEMINI (Paila et al. 2013)). Recent
efforts have been made towards developing more user-friendly graphical software to increase
accessibility to non-bioinformaticians; examples include the commercial suites Ingenuity
(http://www.ingenuity.com/), Alamut (http://www.interactive-biosoftware.com/alamut-visual/),
GoldenHelix SNP (http://goldenhelix.com/SNP_Variation/index.html) and VariantStudio
(http://www.illumina.com/informatics/research/biological-data-interpretation/variantstudio.html), as
well as the open-source packages SNVerGUI (Wang et al. 2012), database.bio (Ou et al. 2015),
gNOME (Lee et al. 2014) and BiERapp (Aleman et al. 2014). However, a disadvantage common to
all these tools is that they strongly depend on a well-defined set of annotations, which limits the
user to a restricted number of pre-defined features. Instead, researchers might be interested in
keeping their personal or in-house annotations for downstream analysis, rather than discarding
them. To our knowledge, only one recently-published software (VCF-Miner) addresses this problem
by skipping the annotation step and focusing on the filtering part (Hart et al. 2015). However, its
installation process requires administrative privileges and knowledge of virtual machines or Docker
containers, it does not natively handle Variant Effect Predictor (VEP,
http://www.ensembl.org/info/docs/tools/vep/index.html) annotations, and it does not allow the user
to query variants that are annotated with a given set of genes of interest.
BrowseVCF overcomes these and other limitations, providing a faster, portable, and simpler
interactive analysis tool to non-bioinformatician researchers. It has been optimised to reduce VCF
loading and indexing time as much as possible by allowing the user to select only the fields of
interest. The filter-history export feature was carefully designed to produce an easy-to-read report
for posterity and reproducibility. It runs natively on multiple operating systems without the need for
administrative privileges or knowledge of sophisticated deployment models. Moreover, our
software has a new feature that allows users to perform keyword-based searches to identify, for
instance, specific consequence terms or associated disorder states.
By applying filters sequentially to the input set of variants, BrowseVCF empowers researchers to
narrow down the amount of putative variants to a small number of candidate disease-causing
alleles.
3
bioRxiv preprint first posted online Dec. 17, 2015; doi: http://dx.doi.org/10.1101/034769. The copyright holder for this preprint
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY 4.0 International license.
Results
Benchmarks
We tested the performance of BrowseVCF on two different file types: an exome trio (proband,
mother, father) and the whole-genome v2.18 of sample NA12878 generated by the “Genome in a
Bottle Consortium” (https://sites.stanford.edu/abms/giab). As shown in Table 1, the amount of time
required to pre-process the VCF file is below 2 minutes for standard exome data and the first query
takes usually less than 30 seconds. Since each subsequent query uses the filtered output of the
previous one, execution time gets shorter. For whole-genome data, pre-processing and index
creation time is substantially longer than for an exome VCF (as expected). Timings were collected
using a single core and differed between the Windows and the GNU/Linux machines. The bundled
WinPython with Windows is not as well optimised as a natively-installed Python, so Windows
performance can be increased by using a different installed Python (this also does not require
administrator privileges). Specifying a higher number of CPUs to use in step 2 speeds up the
process of creating the Wormtables for the fields of interest.

Discussion
The crucial step in variant analysis studies is an accurate prioritisation of the identified mutations,
in order to pinpoint a small number of putative candidates that might be causal for a given disease
or phenotype. BrowseVCF is a fast and intuitive interactive software that enables clinicians and
researchers to browse and query their data without the need of any bioinformatics expertise. The
major advantages of BrowseVCF with respect to similar tools are its clean interface and fast
implementation, both particularly useful when analysing millions of variants from whole-genome
sequencing data. Additionally, BrowseVCF is very straightforward to set up on any machine as it
does not require administrator privileges on a personal machine to install and run. However,
BrowseVCF shows a propensity for shorter execution times on GNU/Linux machines over
Windows machines of similar specification due to operating-system specific differences in the
included WinPython.
A limitation of existing tools is the need to load the entire VCF file, even though no more than a
dozen annotations may typically be used for the analysis. In contrast, BrowseVCF allows the user
4
bioRxiv preprint first posted online Dec. 17, 2015; doi: http://dx.doi.org/10.1101/034769. The copyright holder for this preprint
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY 4.0 International license.
to choose which annotations to process before running the filters and, if needed, to go back and
load additional fields for further filtering steps. Considering that the number of samples and
annotations in VCF files are the main drivers of the load time, being able to restrict the analysis
only to the subset of truly relevant fields saves both computational time and disk space.
In addition, our software can be used both interactively and as part of computational pipelines.
Furthermore, the ability to perform all the analysis locally is a considerable advantage when
dealing with clinically-sensitive files. Although web-based interfaces are easier to operate without
the need for command-line scripts, they can pose a security risk when dealing with sensitive
patient data (Smedley and Robinson 2015). We designed BrowseVCF with flexibility in mind: it can
run on a personal laptop without administrative privileges, as part of a HPC environment, or (in
cases where patient data are available to an organisation behind its firewall) deployed centrally as
a web service. In cases where data sensitivity is less of an issue, BrowseVCF is deployable as a
public online web service. In conclusion, BrowseVCF can significantly improve efficiency in
navigating large data sets to find candidate disease variants, thereby streamlining the clinical
research process.
BrowseVCF is under active development and maintenance. At the time of writing, release version
2.5 is the latest and includes all the features discussed herein. Development and testing continue
for implementation of new features, such as saving and resuming sessions, and global
configuration options. However, the current version shows stability and robustness on multiple
platforms and types of VCF annotations and is ready for deployment in a production environment.

Methods
VCF file loading and pre-processing
The VCF file format is very well-defined (Danecek et al. 2011). It consists of a header section,
containing an arbitrary number of meta-information lines that start with the symbol '#', and of a data
section, containing one line per variant, split into eight mandatory columns: chromosome
(CHROM), 1-based starting position of the variant (POS), unique identifier, if existing (ID),
reference allele in the genome (REF), alternative allele(s) of the variant (ALT), Phred-scaled quality
score (QUAL), flag for passed/failed control checks (FILTER) and variant-specific annotations
5
bioRxiv preprint first posted online Dec. 17, 2015; doi: http://dx.doi.org/10.1101/034769. The copyright holder for this preprint
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY 4.0 International license.
(INFO), which can be an unrestricted number of either flags (present/absent) or key-value pairs.
VCF files containing one or more samples also include a ninth column (FORMAT), used to define
the information enclosed in each subsequent column, and a genotype column for each sample,
regarding the allele combination, the genotype read depth and other metrics.
The loading step within BrowseVCF is straightforward. The user selects a VCF file from his
personal computer through a pop-up dialog box, which is then pre-processed. This step is able to
correctly parse a variety of annotations, including those generated by VEP. BrowseVCF can accept
both uncompressed and compressed (*.gz) VCF file types. The software identifies every
annotation field present in the input VCF file and presents this list to the user, who can then select
the fields of interest that will be used to filter the variants (Fig. 1). Additional fields can be selected
at any time, if needed.
The “Wormtable” Python module (Kelleher et al. 2013) was chosen over other data storage
systems for its efficient use of disk space and for the speed of its queries. However, as one
Wormtable is created per chosen field, selecting fewer fields will reduce the time it takes for
BrowseVCF to process the file. The pre-processing step is also required to convert missing values
('.') to '-1' and to convert CSQ annotations (in case the file was annotated with VEP) into a
Wormtable-compatible format.

Variant prioritization by sequential filtering
Once the file is pre-processed, the user can start to filter variants according to five different query
types (Fig. 2). The first filter lets the user query the data based on a given field of interest. Based
on the type of annotation, different operators can be chosen for a specific cutoff/keyword
(“greater_than”, “less_than”, “equal_to” or “contains_keyword”) and the user can choose whether
to include or exclude variants with missing values. Moreover, the “contains_keyword” option allows
searching for multiple keywords within the same field, which proves very useful when the objective
is to keep variants labelled with different consequence or disease tags, for example. The second
filter extracts variants with a given genotype in one, some or all samples; moreover, it is able to
distinguish between “homozygous reference”, “homozygous alternate” and “heterozygous”. This
feature is particularly useful in the study of inherited and de novo syndromes within a family or
6
bioRxiv preprint first posted online Dec. 17, 2015; doi: http://dx.doi.org/10.1101/034769. The copyright holder for this preprint
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY 4.0 International license.
close relatives. BrowseVCF is able to process both diploid and non-diploid genomes alike. The
third filter can retrieve all variants located within a given region of interest; in this case, the user is
prompted to specify chromosome, start position and end position. This option is particularly useful
to integrate other sources of information, e.g. regions identified by linkage analysis or targeted for
capture. The fourth filter is able to extract either SNPs, InDels, or MNPs. Finally, the fifth query
allows the user to specify a list of genes and retrieves all variants that are either associated
(positive query) or not (negative query) to any of those entries within a specific annotation field.
Filters are applied sequentially, so that a new filter is applied only to the variants that have
successfully passed the previous filter.

Exporting results from the graphical interface
BrowseVCF comes with a user-friendly graphical interface (GUI) that allows researchers to load,
filter, track, and export variants and annotations. Figure 2 displays the filtering interface. The right
panel of the GUI displays variants in a tabular form, whereas the left panel keeps track of the filters
applied and of the number of variants resulting from each query. The user can download the query
history for record-keeping at any point. The output of each query is automatically stored to disk and
can be deleted at any time from the “Filter History” section in the left panel. The right panel shows
a preview of the top 100 variants and all variants can be exported at any time as a tab-separated
text file, compatible with spreadsheet programs like Microsoft Excel.

Technical details
BrowseVCF is free software under the GNU General Public License version 3
(http://www.gnu.org/licenses/gpl-3.0.en.html). It consists of a back-end service of Python scripts
and a web server that runs locally, and of a front end built in JavaScript, CSS, and HTML5,
leveraging popular cross-browser libraries such as AngularJS and Bootstrap. To perform fast and
memory-efficient queries, all filtering operations are executed using Wormtable, a recently-
developed Python module optimised to store genomic data in a compact binary tabular format
through Berkeley DB, and retrieve data quickly by indexing desired fields (Kelleher et al. 2013). It is
this use of Wormtable for data storage that makes BrowseVCF powerful, as future filters and
7
bioRxiv preprint first posted online Dec. 17, 2015; doi: http://dx.doi.org/10.1101/034769. The copyright holder for this preprint
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY 4.0 International license.
enhancements can be implemented relatively easily by amending the indexes that are created in
the Wormtable. The back-end scripts pre-process input VCFs and provide an interface to
communicate with Wormtable in an intuitive manner, both on the command-line and through the
web interface. To enable cross-platform usage, especially on Windows, Wormtable was altered
and optimised. Every operation is carried out locally on the user's computer, using the default
browser; BrowseVCF is supported by all modern browsers, including Firefox and Chrome. The
Windows version of BrowseVCF is shipped along with a lightweight distribution of WinPython, a
bundled environment pre-configured to run directly on Windows. This enables the Windows user to
simply double-click on an executable file without needing administrative permissions to install
Python and necessary custom modules, which greatly improves the accessibility and ease-of-use
of our software. However, since the tool can be configured to run with any web server back-end, it
can also be installed site-wide within an organisation, thereby enabling shared use.

Command-line version of BrowseVCF
BrowseVCF can also be used from command-line as a set of one-off queries, or as part of
automated analysis pipelines. Each Python script is independent from the others and the list of
required arguments can be displayed by typing the script name followed by “--help”. Some of the
scripts are capable of parallel execution by specifying the number of cores at invocation, allowing
BrowseVCF to operate within a high-performance computing (HPC) environment.

Data access
BrowseVCF is freely available for download from
https://github.com/BSGOxford/BrowseVCF/releases/latest .

Acknowledgements
We thank Stefano Lise for advice and discussions, and Jerome Kelleher for significant help with
implementation of new features in Wormtable. We also thank Kate Thomson, John Taylor, Hayley
Mountford, and Alistair Pagnamenta for useful discussion, bug testing, and feedback on
BrowseVCF, and Helen Lockstone for feedback on the manuscript. This work was funded by a
8
bioRxiv preprint first posted online Dec. 17, 2015; doi: http://dx.doi.org/10.1101/034769. The copyright holder for this preprint
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY 4.0 International license.
Wellcome Trust Core Award (090532/Z/09/Z), supporting the Core facilities at The Wellcome Trust
Centre for Human Genetics, including the Oxford Genomics Centre.

Author contributions
S.S. designed the project. S.S. and V.R. implemented the code. S.S. and V.R. wrote the
manuscript. All authors have read and approved the manuscript.

Disclosure declaration
The authors declare that there is no conflict of interest associated with this manuscript.

Figure legends
Figure 1. Screenshot of BrowseVCF step 2 (index creation). The drop-down menu lets the user
browse through the annotation fields present in the input VCF file and select one or more fields to
be used in filtering. A text box allows keyword searching across fields.

Figure 2. Screenshot of BrowseVCF step 3 (filtering). The left panel lists the five available filters,
and expands to allow the user to define various filter options and cutoffs. The “Filter History” panel
keeps track of all the sequential filters applied to the initial data and shows the output number of
variants. The right panel is the output area, which displays the top 100 variants resulting from each
consecutive filter. Fields (shown as columns) can be sorted or hidden if desired.

9
bioRxiv preprint first posted online Dec. 17, 2015; doi: http://dx.doi.org/10.1101/034769. The copyright holder for this preprint
(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
It is made available under a CC-BY 4.0 International license.
Figures
Fig.1

Fig.2

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